Multiple methods for finding best set of mutually compatible BLAST hits

## find-best-hit.py
#!/usr/bin/env python3
'''
For each query listed in a BLAST XML results file, determine how much of the
query sequence is covered by the "best" hit. The best hit is deemed to be the
one in which the summed bitscores of its compatible HSPs is highest. Mutually
compatible HSPs are deemed to be ones that neither overlap nor "cross" each
other.

Usage:
  cat blast_results.xml | find-best-hit.py

Output:
  Values in range [0, 1], with one per line. The nth such value represents the
  fraction of query n covered by the mutually compatible HSPs composing its
  best hit.
'''

import sys
import xml.etree.ElementTree as ET
from collections import defaultdict, namedtuple
import json
from functools import lru_cache
from enum import Enum
import unittest
import os

class Path(Enum):
  without_self = 1
  with_self = 2


class BaseHspSet(object):
  def __init__(self, hsps):
    self._hsps = sorted(hsps, key = lambda a: a.query_start)


class DumbHspSet(BaseHspSet):
  def find_best_combo(self):
    highest_score = 0
    best_combo = None

    for valid_combo in self._compute_hsp_combos(self._hsps):
      score = sum([i.bitscore for i in valid_combo])
      if score > highest_score:
        highest_score = score
        best_combo = valid_combo

    return (highest_score, best_combo)

  def _compute_hsp_combos(self, L):
    sorted_hsps = sorted(L, key = lambda a: a.query_start)
    # Only look at subset of hits to make this computationally tractable.
    return self._combo_r([], sorted_hsps)

  def _combo_r(self, base, remaining):
    if len(remaining) == 0:
      return [base]
    first = remaining[0]
    rest = remaining[1:]

    if len(base) > 0:
      base_query_end = base[-1].query_end
      base_subject_end = base[-1].subject_end
    else:
      base_query_end = 0
      base_subject_end = 0

    if first.query_start > base_query_end and first.subject_start > base_subject_end:
      return self._combo_r(base + [first], rest) + self._combo_r(base, rest)
    else:
      return self._combo_r(base, rest)


class SmartHspSet(BaseHspSet):
  # Segment HSPs into smaller, independent sets. No performance benefit appears
  # in practice -- using segment takes almost exactly as long as not using it
  # -- and so don't bother.
  def _segment(self):
    '''
    Return partition of self._hsps, such that no element (i.e., subset of
    self._hsps) contains an HSP that overlaps another HSP from a different
    element. This is useful because the problem of finding the highest-scoring
    subset of HSPs can then be solved independently for each subset forming the
    partition without impairing the answer's optimality.
    '''
    segments = []
    i = 0
    n = len(self._hsps)

    while i < n:
      candidate = self._hsps[i]
      max_query_end = candidate.query_end
      max_subject_end = candidate.subject_end
      conflicting_query_end = candidate.query_end
      conflicting_subject_end = candidate.subject_end
      last_conflicting = i

      for j in range(i + 1, n):
        other = self._hsps[j]
        max_query_end = max(max_query_end, other.query_end)
        max_subject_end = max(max_subject_end, other.subject_end)
        if other.query_start <= conflicting_query_end \
        or other.subject_start <= conflicting_subject_end:
          conflicting_query_end = max_query_end
          conflicting_subject_end = max_subject_end
          last_conflicting = j

      next_compatible = last_conflicting + 1
      segments.append(tuple(range(i, next_compatible)))
      i = next_compatible

    return segments

  @lru_cache(maxsize=None)
  def _find_compatible(self, target, indices):
    '''Find all hsps that don't overlap or cross `target`. Such HSPs are not
    guaranteed to be mutually compatible, but are guaranteed only to be
    compatible with `target`.'''
    compatible = []
    target_hsp = self._hsps[target]
    for index in indices:
      # Don't define target as overlapping itself.
      if index == target:
        continue
      test_hsp = self._hsps[index]

      if target_hsp.query_start <= test_hsp.query_start:
        first, second = target_hsp, test_hsp
      else:
        first, second = test_hsp, target_hsp

      overlap = second.query_start <= first.query_end
      overlap = overlap or second.subject_start <= first.subject_end
      if not overlap:
        compatible.append(index)

    return tuple(compatible)

  def _reconstruct_soln(self, indices):
    return self._reconstruct_soln_r(indices, [])

  def _reconstruct_soln_r(self, indices, soln):
    if len(indices) <= 1:
      return indices

    indices = tuple(indices)
    if self._combo_soln[indices] == Path.without_self:
      return self._reconstruct_soln_r(indices[:-1], soln)
    else:
      last_index = indices[-1]
      compatible = self._find_compatible(last_index, indices)
      soln = self._reconstruct_soln_r(compatible, soln)
      return soln + (last_index,)

  def find_best_combo(self):
    indices = range(len(self._hsps))
    self._best_values  = {}
    self._combo_soln   = {}

    score = self._find_best_combo_r(tuple(indices))
    soln = self._reconstruct_soln(indices)
    soln = [self._hsps[i] for i in soln]

    return (score, soln)

  @lru_cache(maxsize=None)
  def _find_best_combo_r(self, indices):
    # Must check for len == 0, which will occur if we're looking at only two
    # hsps that are incompatible with each other. When looking at the
    # second, the call self._find_best_combo_r(compatible) will recurse on an
    # empty tuple (since "compatible" doesn't include the self, and it won't
    # include the preceding (incompatible) hsp).
    if len(indices) == 0:
      return 0
    if len(indices) == 1:
      self._combo_soln[indices] = Path.with_self
      return self._hsps[indices[0]].bitscore

    last_index = indices[-1]
    without_self_indices = indices[:-1]
    # This order retains sort order, since indices[-1] will be after all other
    # elements in indices.
    compatible = self._find_compatible(last_index, indices)

    best_without_self = self._find_best_combo_r(without_self_indices)
    best_with_self    = self._find_best_combo_r(compatible) + self._hsps[indices[-1]].bitscore

    if best_without_self >= best_with_self:
      self._best_values[indices] = best_without_self
      self._combo_soln[indices]  = Path.without_self
    else:
      self._best_values[indices] = best_with_self
      self._combo_soln[indices]  = Path.with_self

    return self._best_values[indices]


Query = namedtuple('Query', [
  'query_def',
  'query_length',
  'hits',
])

Hit = namedtuple('Hit', [
  'subject_length',
  'subject_def',
  'hsps',
])

Hsp = namedtuple('Hsp', [
  'query_start',
  'query_end',
  'query_frame',
  'subject_start',
  'subject_end',
  'subject_frame',
  'alignment_length',
  'bitscore',
])


class BlastParser(object):
  def _determine_strand(self, frame):
    if frame > 0:
      return '+'
    elif frame < 0:
      return '-'
    else:
      return '.'

  def parse(self, blast_xml):
    tree = ET.parse(blast_xml)
    root = tree.getroot()
    queries = []

    for iteration_elem in root.find('BlastOutput_iterations').iter('Iteration'):
      query = Query(
        query_length = int(iteration_elem.find('Iteration_query-len').text),
        query_def    = iteration_elem.find('Iteration_query-def').text,
        hits         = []
      )

      hit_elems = iteration_elem.find('Iteration_hits')
      if hit_elems is None:
        continue
      for hit_elem in hit_elems.iter('Hit'):
        hit = Hit(
          subject_length = int(hit_elem.find('Hit_len').text),
          subject_def    = hit_elem.find('Hit_def').text,
          hsps           = defaultdict(lambda: []),
        )

        for hsp_elem in hit_elem.find('Hit_hsps').iter('Hsp'):
          hsp = Hsp(
            query_frame = int(hsp_elem.find('Hsp_query-frame').text),
            subject_frame = int(hsp_elem.find('Hsp_hit-frame').text),
            alignment_length = int(hsp_elem.find('Hsp_align-len').text),
            query_start = int(hsp_elem.find('Hsp_query-from').text),
            query_end = int(hsp_elem.find('Hsp_query-to').text),
            subject_start = int(hsp_elem.find('Hsp_hit-from').text),
            subject_end = int(hsp_elem.find('Hsp_hit-to').text),
            bitscore = float(hsp_elem.find('Hsp_bit-score').text),
          )

          # Ensure sequence start coordinate is always less than sequence end
          # coordinate (which they won't be for sequences on minus strand).
          for seq_type in ('query', 'subject'):
            if getattr(hsp, '%s_frame' % seq_type) < 0:
              start_key = '%s_start' % seq_type
              end_key   = '%s_end' % seq_type
              start     = getattr(hsp, start_key)
              end       = getattr(hsp, end_key)

              if seq_type == 'query':
                length = query.query_length
              elif seq_type == 'subject':
                length = hit.subject_length

              if start > end:
                tmp = start
                start = end
                end = tmp

              hsp = hsp._replace(**{
                start_key: length - end + 1,
                end_key:   length - start + 1,
              })

          key = self._determine_strand(hsp.query_frame) + \
                self._determine_strand(hsp.subject_frame)
          hit.hsps[key].append(hsp)
        query.hits.append(hit)
      queries.append(query)
    return queries


class Scorer(object):
  def print_query_scores(self, queries):
    for query in queries:
      best_query_proportion = 0

      for hit in query.hits:
        for strand_combo, segregated_hsps in hit.hsps.items():
          query_proportion = self._calc_query_proportion(segregated_hsps, query.query_length)
          if query_proportion > best_query_proportion:
            best_query_proportion = query_proportion
      print(best_query_proportion)


class CompatibleHspScorer(Scorer):
  def _calc_query_proportion(self, hsps, query_len):
    #hsp_set = DumbHspSet(hsps)
    hsp_set = SmartHspSet(hsps)
    score, soln = hsp_set.find_best_combo()
    hsps_size = sum([h.query_end - h.query_start + 1 for h in soln])
    return hsps_size / query_len


class HspUnionScorer(Scorer):
  def _calc_query_proportion(self, hsps, query_len):
    intervals = [(hsp.query_start, hsp.query_end) for hsp in hsps]
    interval_union = self._compute_interval_union(intervals)
    size_sum = self._compute_interval_size_sum(interval_union)
    return size_sum / query_len

  def _compute_interval_union(self, intervals):
    '''
    Compute union of overlapping intervals. Assume both endpoints are inclusive.
    '''
    points = []
    for start, end in intervals:
      points.append((start, 'start'))
      points.append((end,   'end'))
    points.sort(key=lambda p: p[0])

    # Collapse overlapping intervals.
    open_intervals = 0
    current_open_start = None
    interval_union = []
    for coord, ptype in points:
      if ptype == 'start':
        if open_intervals == 0:
          current_open_start = coord
        open_intervals += 1
      elif ptype == 'end':
        open_intervals -= 1
        if open_intervals == 0:
          interval_union.append((current_open_start, coord))
      else:
        raise Exception('Unknown point type: %s' % ptype)

    # Merge adjacent intervals.
    merged_union = []
    open_start, last_end = interval_union[0]
    for start, end in interval_union[1:]:
      assert start >= last_end
      # Last interval and current interval are not immediately adjacent.
      if start > last_end + 1:
        merged_union.append((open_start, last_end))
        open_start = start
      last_end = end
    merged_union.append((open_start, last_end))

    return merged_union

  def _compute_interval_size_sum(self, intervals):
    interval_sum = 0
    for start, end in intervals:
      interval_sum += end - start + 1
    return interval_sum


class TestSmartHspSet(unittest.TestCase):
  def test_simple(self):
    hsps = [
      Hsp(query_start=119, query_end=249, subject_start=1157, subject_end=1287, bitscore=243.031, subject_frame=1, query_frame=1, alignment_length=5),
      Hsp(query_start=244, query_end=345, subject_start=845, subject_end=946, bitscore=189.479, subject_frame=1, query_frame=1, alignment_length=5),
      Hsp(query_start=17, query_end=118, subject_start=1901, subject_end=2002, bitscore=189.479, subject_frame=1, query_frame=1, alignment_length=5)
    ]
    hsp_set = SmartHspSet(hsps)
    score, soln = hsp_set.find_best_combo()
    self.assertAlmostEqual(243.031, score)
    self.assertEqual([hsps[0]], soln)

  def test_simple_two(self):
    hsps = [
      Hsp(query_start=346, query_end=854, subject_start=9427, subject_end=9935, bitscore=470.169, subject_frame=1, query_frame=1, alignment_length=5),
      Hsp(query_start=1, query_end=339, subject_start=231024, subject_end=231362, bitscore=178.399, subject_frame=1, query_frame=1, alignment_length=5)
    ]
    hsp_set = SmartHspSet(hsps)
    score, soln = hsp_set.find_best_combo()
    self.assertAlmostEqual(470.169, score)
    self.assertEqual([hsps[0]], soln)

  def _test_against_blast_xml(self, xml_filename, strand_pair, hsp_len, expected_score, expected_soln):
    xml_path = os.path.join(os.path.dirname(__file__), 'test-cases', xml_filename)
    queries = BlastParser().parse(xml_path)

    self.assertEqual(1, len(queries))
    query = queries[0]
    self.assertEqual(1, len(query.hits))
    hit = query.hits[0]
    self.assertEqual(1, len(hit.hsps.keys()))
    hsps = hit.hsps[strand_pair]
    self.assertEqual(hsp_len, len(hsps))

    hsp_set = SmartHspSet(hsps)
    score, soln = hsp_set.find_best_combo()
    self.assertAlmostEqual(expected_score, score)
    self.assertEqual(expected_soln, soln)

  def test_reverse_complement(self):
    self._test_against_blast_xml(
      xml_filename = 'reverse_complement.blast.xml',
      strand_pair = '+-',
      hsp_len = 2,
      expected_score = 505.562,
      expected_soln = [ Hsp(
        query_start=5,
        query_end=8,
        query_frame=1,
        subject_start=22,
        subject_end=22,
        subject_frame=-1,
        alignment_length=275,
        bitscore=505.562) ])

  def test_problematic(self):
    self._test_against_blast_xml(
      xml_filename = 'problematic.blast.xml',
      strand_pair = '++',
      hsp_len = 4,
      expected_score = 1699.312,
      expected_soln = [ Hsp(
        query_start=1631,
        query_end=1949,
        query_frame=1,
        subject_start=1,
        subject_end=319,
        subject_frame=1,
        alignment_length=275,
        bitscore=573.582
      ),
      Hsp(
        query_start=2687,
        query_end=3406,
        query_frame=1,
        subject_start=373,
        subject_end=1075,
        subject_frame=1,
        alignment_length=275,
        bitscore=1125.73
      ) ])


class TestHspUnionScorer(unittest.TestCase):
  def test_basic(self):
    cases = (
      # Basic example
      (
        [(1, 16), (7, 15), (5, 10), (13, 16), (19, 21), (19, 20)],
        [(1, 16), (19, 21)],
        19
      ),
      # Correctly handle endpoints, assuming start-inclusive, end-exclusive
      (
        [(1, 15), (15, 20)],
        [(1, 20)],
        20
      ),
      # Correctly handle endpoints, assuming start-inclusive, end-inclusive
      (
        [(1, 15), (16, 20)],
        [(1, 20)],
        20
      ),
      # Ensure overlapping interavls handled properly
      (
        [(1, 15), (14, 20)],
        [(1, 20)],
        20
      )
    )

    for intervals, expected_union, expected_size_sum in cases:
      scorer = HspUnionScorer()
      result_union = scorer._compute_interval_union(intervals)
      result_size_sum = scorer._compute_interval_size_sum(result_union)
      self.assertEqual(expected_union, result_union)
      self.assertEqual(expected_size_sum, result_size_sum)


def test_smart_vs_dumb_algorithm(queries):
  decimal_places = 7

  for query in queries:
    for hit in query.hits:
      for strand_combo, segregated_hsps in hit.hsps.items():
        # Dumb algorithm takes exponential time (given 2^n combinations), so avoid
        # computing more than 2^20 combos.
        if len(segregated_hsps) > 20:
          continue

        smart_hsp_set = SmartHspSet(segregated_hsps)
        dumb_hsp_set = DumbHspSet(segregated_hsps)
        smart_score, smart_soln   = hsp_set.find_best_combo()
        dumb_score, dumb_soln = dumb_hsp_set.find_best_combo()
        if round(dumb_score - smart_score, decimal_places) != 0:
          from pprint import pprint
          pprint(segregated_hsps, stream=sys.stderr)
          raise Exception('Scores unequal (smart=%s, dumb=%s)' % (smart_score, dumb_score))

def main():
  class RunType(Enum):
    normal = 1
    compare_dp_vs_dumb = 2
    test_cases = 3

  run_type = RunType.normal
  #run_type = RunType.compare_dp_vs_dumb
  #run_type = RunType.test_cases

  if run_type == RunType.normal:
    queries = BlastParser().parse(sys.stdin)
    #scorer = HspUnionScorer()
    scorer = CompatibleHspScorer()
    scorer.print_query_scores(queries)

  elif run_type == RunType.compare_dp_vs_dumb:
    queries = BlastParser().parse(sys.stdin)
    test_smart_vs_dumb_algorithm(queries)

  elif run_type == RunType.test_cases:
    unittest.main()

if __name__ == '__main__':
  main()
	#!/usr/bin/env python3
	'''
	For each query listed in a BLAST XML results file, determine how much of the
	query sequence is covered by the "best" hit. The best hit is deemed to be the
	one in which the summed bitscores of its compatible HSPs is highest. Mutually
	compatible HSPs are deemed to be ones that neither overlap nor "cross" each
	other.

	Usage:
	cat blast_results.xml \| find-best-hit.py

	Output:
	Values in range [0, 1], with one per line. The nth such value represents the
	fraction of query n covered by the mutually compatible HSPs composing its
	best hit.
	'''

	import sys
	import xml.etree.ElementTree as ET
	from collections import defaultdict, namedtuple
	import json
	from functools import lru_cache
	from enum import Enum
	import unittest
	import os

	class Path(Enum):
	without_self = 1
	with_self = 2


	class BaseHspSet(object):
	def __init__(self, hsps):
	self._hsps = sorted(hsps, key = lambda a: a.query_start)


	class DumbHspSet(BaseHspSet):
	def find_best_combo(self):
	highest_score = 0
	best_combo = None

	for valid_combo in self._compute_hsp_combos(self._hsps):
	score = sum([i.bitscore for i in valid_combo])
	if score > highest_score:
	highest_score = score
	best_combo = valid_combo

	return (highest_score, best_combo)

	def _compute_hsp_combos(self, L):
	sorted_hsps = sorted(L, key = lambda a: a.query_start)
	# Only look at subset of hits to make this computationally tractable.
	return self._combo_r([], sorted_hsps)

	def _combo_r(self, base, remaining):
	if len(remaining) == 0:
	return [base]
	first = remaining[0]
	rest = remaining[1:]

	if len(base) > 0:
	base_query_end = base[-1].query_end
	base_subject_end = base[-1].subject_end
	else:
	base_query_end = 0
	base_subject_end = 0

	if first.query_start > base_query_end and first.subject_start > base_subject_end:
	return self._combo_r(base + [first], rest) + self._combo_r(base, rest)
	else:
	return self._combo_r(base, rest)


	class SmartHspSet(BaseHspSet):
	# Segment HSPs into smaller, independent sets. No performance benefit appears
	# in practice -- using segment takes almost exactly as long as not using it
	# -- and so don't bother.
	def _segment(self):
	'''
	Return partition of self._hsps, such that no element (i.e., subset of
	self._hsps) contains an HSP that overlaps another HSP from a different
	element. This is useful because the problem of finding the highest-scoring
	subset of HSPs can then be solved independently for each subset forming the
	partition without impairing the answer's optimality.
	'''
	segments = []
	i = 0
	n = len(self._hsps)

	while i < n:
	candidate = self._hsps[i]
	max_query_end = candidate.query_end
	max_subject_end = candidate.subject_end
	conflicting_query_end = candidate.query_end
	conflicting_subject_end = candidate.subject_end
	last_conflicting = i

	for j in range(i + 1, n):
	other = self._hsps[j]
	max_query_end = max(max_query_end, other.query_end)
	max_subject_end = max(max_subject_end, other.subject_end)
	if other.query_start <= conflicting_query_end \
	or other.subject_start <= conflicting_subject_end:
	conflicting_query_end = max_query_end
	conflicting_subject_end = max_subject_end
	last_conflicting = j

	next_compatible = last_conflicting + 1
	segments.append(tuple(range(i, next_compatible)))
	i = next_compatible

	return segments

	@lru_cache(maxsize=None)
	def _find_compatible(self, target, indices):
	'''Find all hsps that don't overlap or cross `target`. Such HSPs are not
	guaranteed to be mutually compatible, but are guaranteed only to be
	compatible with `target`.'''
	compatible = []
	target_hsp = self._hsps[target]
	for index in indices:
	# Don't define target as overlapping itself.
	if index == target:
	continue
	test_hsp = self._hsps[index]

	if target_hsp.query_start <= test_hsp.query_start:
	first, second = target_hsp, test_hsp
	else:
	first, second = test_hsp, target_hsp

	overlap = second.query_start <= first.query_end
	overlap = overlap or second.subject_start <= first.subject_end
	if not overlap:
	compatible.append(index)

	return tuple(compatible)

	def _reconstruct_soln(self, indices):
	return self._reconstruct_soln_r(indices, [])

	def _reconstruct_soln_r(self, indices, soln):
	if len(indices) <= 1:
	return indices

	indices = tuple(indices)
	if self._combo_soln[indices] == Path.without_self:
	return self._reconstruct_soln_r(indices[:-1], soln)
	else:
	last_index = indices[-1]
	compatible = self._find_compatible(last_index, indices)
	soln = self._reconstruct_soln_r(compatible, soln)
	return soln + (last_index,)

	def find_best_combo(self):
	indices = range(len(self._hsps))
	self._best_values = {}
	self._combo_soln = {}

	score = self._find_best_combo_r(tuple(indices))
	soln = self._reconstruct_soln(indices)
	soln = [self._hsps[i] for i in soln]

	return (score, soln)

	@lru_cache(maxsize=None)
	def _find_best_combo_r(self, indices):
	# Must check for len == 0, which will occur if we're looking at only two
	# hsps that are incompatible with each other. When looking at the
	# second, the call self._find_best_combo_r(compatible) will recurse on an
	# empty tuple (since "compatible" doesn't include the self, and it won't
	# include the preceding (incompatible) hsp).
	if len(indices) == 0:
	return 0
	if len(indices) == 1:
	self._combo_soln[indices] = Path.with_self
	return self._hsps[indices[0]].bitscore

	last_index = indices[-1]
	without_self_indices = indices[:-1]
	# This order retains sort order, since indices[-1] will be after all other
	# elements in indices.
	compatible = self._find_compatible(last_index, indices)

	best_without_self = self._find_best_combo_r(without_self_indices)
	best_with_self = self._find_best_combo_r(compatible) + self._hsps[indices[-1]].bitscore

	if best_without_self >= best_with_self:
	self._best_values[indices] = best_without_self
	self._combo_soln[indices] = Path.without_self
	else:
	self._best_values[indices] = best_with_self
	self._combo_soln[indices] = Path.with_self

	return self._best_values[indices]


	Query = namedtuple('Query', [
	'query_def',
	'query_length',
	'hits',
	])

	Hit = namedtuple('Hit', [
	'subject_length',
	'subject_def',
	'hsps',
	])

	Hsp = namedtuple('Hsp', [
	'query_start',
	'query_end',
	'query_frame',
	'subject_start',
	'subject_end',
	'subject_frame',
	'alignment_length',
	'bitscore',
	])


	class BlastParser(object):
	def _determine_strand(self, frame):
	if frame > 0:
	return '+'
	elif frame < 0:
	return '-'
	else:
	return '.'

	def parse(self, blast_xml):
	tree = ET.parse(blast_xml)
	root = tree.getroot()
	queries = []

	for iteration_elem in root.find('BlastOutput_iterations').iter('Iteration'):
	query = Query(
	query_length = int(iteration_elem.find('Iteration_query-len').text),
	query_def = iteration_elem.find('Iteration_query-def').text,
	hits = []
	)

	hit_elems = iteration_elem.find('Iteration_hits')
	if hit_elems is None:
	continue
	for hit_elem in hit_elems.iter('Hit'):
	hit = Hit(
	subject_length = int(hit_elem.find('Hit_len').text),
	subject_def = hit_elem.find('Hit_def').text,
	hsps = defaultdict(lambda: []),
	)

	for hsp_elem in hit_elem.find('Hit_hsps').iter('Hsp'):
	hsp = Hsp(
	query_frame = int(hsp_elem.find('Hsp_query-frame').text),
	subject_frame = int(hsp_elem.find('Hsp_hit-frame').text),
	alignment_length = int(hsp_elem.find('Hsp_align-len').text),
	query_start = int(hsp_elem.find('Hsp_query-from').text),
	query_end = int(hsp_elem.find('Hsp_query-to').text),
	subject_start = int(hsp_elem.find('Hsp_hit-from').text),
	subject_end = int(hsp_elem.find('Hsp_hit-to').text),
	bitscore = float(hsp_elem.find('Hsp_bit-score').text),
	)

	# Ensure sequence start coordinate is always less than sequence end
	# coordinate (which they won't be for sequences on minus strand).
	for seq_type in ('query', 'subject'):
	if getattr(hsp, '%s_frame' % seq_type) < 0:
	start_key = '%s_start' % seq_type
	end_key = '%s_end' % seq_type
	start = getattr(hsp, start_key)
	end = getattr(hsp, end_key)

	if seq_type == 'query':
	length = query.query_length
	elif seq_type == 'subject':
	length = hit.subject_length

	if start > end:
	tmp = start
	start = end
	end = tmp

	hsp = hsp._replace(**{
	start_key: length - end + 1,
	end_key: length - start + 1,
	})

	key = self._determine_strand(hsp.query_frame) + \
	self._determine_strand(hsp.subject_frame)
	hit.hsps[key].append(hsp)
	query.hits.append(hit)
	queries.append(query)
	return queries


	class Scorer(object):
	def print_query_scores(self, queries):
	for query in queries:
	best_query_proportion = 0

	for hit in query.hits:
	for strand_combo, segregated_hsps in hit.hsps.items():
	query_proportion = self._calc_query_proportion(segregated_hsps, query.query_length)
	if query_proportion > best_query_proportion:
	best_query_proportion = query_proportion
	print(best_query_proportion)


	class CompatibleHspScorer(Scorer):
	def _calc_query_proportion(self, hsps, query_len):
	#hsp_set = DumbHspSet(hsps)
	hsp_set = SmartHspSet(hsps)
	score, soln = hsp_set.find_best_combo()
	hsps_size = sum([h.query_end - h.query_start + 1 for h in soln])
	return hsps_size / query_len


	class HspUnionScorer(Scorer):
	def _calc_query_proportion(self, hsps, query_len):
	intervals = [(hsp.query_start, hsp.query_end) for hsp in hsps]
	interval_union = self._compute_interval_union(intervals)
	size_sum = self._compute_interval_size_sum(interval_union)
	return size_sum / query_len

	def _compute_interval_union(self, intervals):
	'''
	Compute union of overlapping intervals. Assume both endpoints are inclusive.
	'''
	points = []
	for start, end in intervals:
	points.append((start, 'start'))
	points.append((end, 'end'))
	points.sort(key=lambda p: p[0])

	# Collapse overlapping intervals.
	open_intervals = 0
	current_open_start = None
	interval_union = []
	for coord, ptype in points:
	if ptype == 'start':
	if open_intervals == 0:
	current_open_start = coord
	open_intervals += 1
	elif ptype == 'end':
	open_intervals -= 1
	if open_intervals == 0:
	interval_union.append((current_open_start, coord))
	else:
	raise Exception('Unknown point type: %s' % ptype)

	# Merge adjacent intervals.
	merged_union = []
	open_start, last_end = interval_union[0]
	for start, end in interval_union[1:]:
	assert start >= last_end
	# Last interval and current interval are not immediately adjacent.
	if start > last_end + 1:
	merged_union.append((open_start, last_end))
	open_start = start
	last_end = end
	merged_union.append((open_start, last_end))

	return merged_union

	def _compute_interval_size_sum(self, intervals):
	interval_sum = 0
	for start, end in intervals:
	interval_sum += end - start + 1
	return interval_sum


	class TestSmartHspSet(unittest.TestCase):
	def test_simple(self):
	hsps = [
	Hsp(query_start=119, query_end=249, subject_start=1157, subject_end=1287, bitscore=243.031, subject_frame=1, query_frame=1, alignment_length=5),
	Hsp(query_start=244, query_end=345, subject_start=845, subject_end=946, bitscore=189.479, subject_frame=1, query_frame=1, alignment_length=5),
	Hsp(query_start=17, query_end=118, subject_start=1901, subject_end=2002, bitscore=189.479, subject_frame=1, query_frame=1, alignment_length=5)
	]
	hsp_set = SmartHspSet(hsps)
	score, soln = hsp_set.find_best_combo()
	self.assertAlmostEqual(243.031, score)
	self.assertEqual([hsps[0]], soln)

	def test_simple_two(self):
	hsps = [
	Hsp(query_start=346, query_end=854, subject_start=9427, subject_end=9935, bitscore=470.169, subject_frame=1, query_frame=1, alignment_length=5),
	Hsp(query_start=1, query_end=339, subject_start=231024, subject_end=231362, bitscore=178.399, subject_frame=1, query_frame=1, alignment_length=5)
	]
	hsp_set = SmartHspSet(hsps)
	score, soln = hsp_set.find_best_combo()
	self.assertAlmostEqual(470.169, score)
	self.assertEqual([hsps[0]], soln)

	def _test_against_blast_xml(self, xml_filename, strand_pair, hsp_len, expected_score, expected_soln):
	xml_path = os.path.join(os.path.dirname(__file__), 'test-cases', xml_filename)
	queries = BlastParser().parse(xml_path)

	self.assertEqual(1, len(queries))
	query = queries[0]
	self.assertEqual(1, len(query.hits))
	hit = query.hits[0]
	self.assertEqual(1, len(hit.hsps.keys()))
	hsps = hit.hsps[strand_pair]
	self.assertEqual(hsp_len, len(hsps))

	hsp_set = SmartHspSet(hsps)
	score, soln = hsp_set.find_best_combo()
	self.assertAlmostEqual(expected_score, score)
	self.assertEqual(expected_soln, soln)

	def test_reverse_complement(self):
	self._test_against_blast_xml(
	xml_filename = 'reverse_complement.blast.xml',
	strand_pair = '+-',
	hsp_len = 2,
	expected_score = 505.562,
	expected_soln = [ Hsp(
	query_start=5,
	query_end=8,
	query_frame=1,
	subject_start=22,
	subject_end=22,
	subject_frame=-1,
	alignment_length=275,
	bitscore=505.562) ])

	def test_problematic(self):
	self._test_against_blast_xml(
	xml_filename = 'problematic.blast.xml',
	strand_pair = '++',
	hsp_len = 4,
	expected_score = 1699.312,
	expected_soln = [ Hsp(
	query_start=1631,
	query_end=1949,
	query_frame=1,
	subject_start=1,
	subject_end=319,
	subject_frame=1,
	alignment_length=275,
	bitscore=573.582
	),
	Hsp(
	query_start=2687,
	query_end=3406,
	query_frame=1,
	subject_start=373,
	subject_end=1075,
	subject_frame=1,
	alignment_length=275,
	bitscore=1125.73
	) ])


	class TestHspUnionScorer(unittest.TestCase):
	def test_basic(self):
	cases = (
	# Basic example
	(
	[(1, 16), (7, 15), (5, 10), (13, 16), (19, 21), (19, 20)],
	[(1, 16), (19, 21)],
	19
	),
	# Correctly handle endpoints, assuming start-inclusive, end-exclusive
	(
	[(1, 15), (15, 20)],
	[(1, 20)],
	20
	),
	# Correctly handle endpoints, assuming start-inclusive, end-inclusive
	(
	[(1, 15), (16, 20)],
	[(1, 20)],
	20
	),
	# Ensure overlapping interavls handled properly
	(
	[(1, 15), (14, 20)],
	[(1, 20)],
	20
	)
	)

	for intervals, expected_union, expected_size_sum in cases:
	scorer = HspUnionScorer()
	result_union = scorer._compute_interval_union(intervals)
	result_size_sum = scorer._compute_interval_size_sum(result_union)
	self.assertEqual(expected_union, result_union)
	self.assertEqual(expected_size_sum, result_size_sum)


	def test_smart_vs_dumb_algorithm(queries):
	decimal_places = 7

	for query in queries:
	for hit in query.hits:
	for strand_combo, segregated_hsps in hit.hsps.items():
	# Dumb algorithm takes exponential time (given 2^n combinations), so avoid
	# computing more than 2^20 combos.
	if len(segregated_hsps) > 20:
	continue

	smart_hsp_set = SmartHspSet(segregated_hsps)
	dumb_hsp_set = DumbHspSet(segregated_hsps)
	smart_score, smart_soln = hsp_set.find_best_combo()
	dumb_score, dumb_soln = dumb_hsp_set.find_best_combo()
	if round(dumb_score - smart_score, decimal_places) != 0:
	from pprint import pprint
	pprint(segregated_hsps, stream=sys.stderr)
	raise Exception('Scores unequal (smart=%s, dumb=%s)' % (smart_score, dumb_score))

	def main():
	class RunType(Enum):
	normal = 1
	compare_dp_vs_dumb = 2
	test_cases = 3

	run_type = RunType.normal
	#run_type = RunType.compare_dp_vs_dumb
	#run_type = RunType.test_cases

	if run_type == RunType.normal:
	queries = BlastParser().parse(sys.stdin)
	#scorer = HspUnionScorer()
	scorer = CompatibleHspScorer()
	scorer.print_query_scores(queries)

	elif run_type == RunType.compare_dp_vs_dumb:
	queries = BlastParser().parse(sys.stdin)
	test_smart_vs_dumb_algorithm(queries)

	elif run_type == RunType.test_cases:
	unittest.main()

	if __name__ == '__main__':
	main()